Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
arXiv:2510.09405v2 Announce Type: replace Abstract: Radio frequency fingerprint identification (RFFI) is a key technique for wireless network security, leveraging intrinsic hardware imperfections to enable transmitter identification. Although deep neural networks are effective at extracting discriminative RF features, their performance is significantly affected by receiver-induced variability in practical deployments. In real-world scenarios, RF signals inherently entangle transmitter-specific characteristics with receiver-dependent distortions, leading models to capture receiver-related patte
The increasing reliance on wireless networks and the sophistication of signal analysis necessitate more robust identification and security measures for RF devices.
Improving RF fingerprint identification helps bolster wireless network security and allows for more precise identification of transmitters, which has implications across various sectors.
This research could lead to more reliable and generalized RF fingerprinting systems, reducing vulnerabilities caused by receiver-induced variability in identification processes.
- · Wireless network security providers
- · Military and intelligence agencies
- · Telecommunications companies
- · Hardware manufacturers with unique RF signatures
- · Malicious actors using spoofed or anonymous RF transmissions
- · Those relying on receiver-dependent security vulnerabilities
Enhanced security for critical wireless infrastructure and communications.
Potential for new regulations or standards around RF device identification for security and compliance.
Increased difficulty for covert operations reliant on untraceable wireless communication, influencing geopolitical strategies.
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Read at arXiv cs.LG